move out compute jobs #8
Description
This can be its own package,
#' MODISA chlorophyll-a in the southern hemisphere.
#'
#' Compute chlorophyll data in the L3 bins, using both Johnson and Oceancolor algorithms.
#'
#' Use `raadtools::ocfiles(product = "MODISA", varname = "RRS", type = "L3b")` to get all daily MODISA L3 bin RRS files, and
#' explore the `date` column to generate a set of `days` for this function.
#' @param days dates to calculate
#' @param maxbin a maximum bin to use, for specialist use only
#' @return tibble of bins and chlorophyll-a values
#' @export
#' @importFrom tibble tibble
#' @importFrom progress progress_bar
#' @importFrom dplyr mutate filter select transmute
#' @importFrom rlang .data
chla_compute <- function(days, maxbin = NULL) {
## data frame of all L3 RRS files for MODISA
files <- ocfiles(time.resolution = "daily", product = "MODISA", varname = "RRS", type = "L3b", bz2.rm = TRUE, ext = "nc")
days <- as.Date(as.POSIXct(days, tz = "GMT"))
files <- files[as.Date(files$date) %in% days, ]
## initialize the bin logic for MODISA
init <- initbin(NUMROWS = 4320)
## counts up from the south
if (is.null(maxbin)) maxbin <- init$totbin/2
## accumulate sum and count for mean
count_rj <- cumul_rj <- numeric(maxbin)
count_nasa <- cumul_nasa <- numeric(maxbin)
## track progress
pb <- progress_bar$new(total = nrow(files))
for (ifile in seq(nrow(files))) {
d <- read_L3_file(files$fullname[ifile], compound_vars = c("Rrs_443", "Rrs_488", "Rrs_555", "Rrs_547")) %>%
filter(.data$bin_num <= maxbin) %>%
mutate(chla_johnson = chla(.data, sensor = "MODISA", algo = "johnson")) %>%
mutate(chla_nasa = chla(.data, sensor = "MODISA", algo = "oceancolor")) %>%
dplyr::select(.data$bin_num, .data$chla_johnson, .data$chla_nasa) %>%
filter(.data$chla_johnson > 0)
## we accumulate outside of the data frame to ensure speed
## the bin_num indexes the vector structure directly since the south pole is bin 1
count_rj[d$bin_num] <- count_rj[d$bin_num] + 1
cumul_rj[d$bin_num] <- cumul_rj[d$bin_num] + d$chla_johnson
count_nasa[d$bin_num] <- count_nasa[d$bin_num] + 1
cumul_nasa[d$bin_num] <- cumul_nasa[d$bin_num] + d$chla_nasa
## track progress
pb$tick()
}
tibble(bin_num = seq_len(maxbin), count_nasa = count_nasa, cumul_nasa = cumul_nasa,
count_rj = count_rj, cumul_rj = cumul_rj) %>% filter(.data$count_rj > 0)
}